PyTorch实现卷积神经网络的搭建详解

nn.Conv2d( in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, ) 

MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) 

import os import numpy as np import torch import torch.nn as nn import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt # 循环世代 EPOCH = 20 BATCH_SIZE = 50 # 下载mnist数据集 train_data = torchvision.datasets.MNIST(root='./mnist/',train=True,transform=torchvision.transforms.ToTensor(),download=True,) # (60000, 28, 28) print(train_data.train_data.size()) # (60000) print(train_data.train_labels.size()) train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) # 测试集 test_data = torchvision.datasets.MNIST(root='./mnist/', train=False) # (2000, 1, 28, 28) # 标准化 test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255. test_y = test_data.test_labels[:2000] # 建立pytorch神经网络 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() #----------------------------# #   第一部分卷积 #----------------------------# self.conv1 = nn.Sequential( nn.Conv2d( in_channels=1, out_channels=32, kernel_size=5, stride=1, padding=2, dilation=1 ), nn.ReLU(), nn.MaxPool2d(kernel_size=2), ) #----------------------------# #   第二部分卷积 #----------------------------# self.conv2 = nn.Sequential( nn.Conv2d( in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, dilation=1 ), nn.ReLU(), nn.MaxPool2d(kernel_size=2), ) #----------------------------# #   全连接+池化+全连接 #----------------------------# self.ful1 = nn.Linear(64 * 7 * 7, 512) self.drop = nn.Dropout(0.5) self.ful2 = nn.Sequential(nn.Linear(512, 10),nn.Softmax()) #----------------------------# #   前向传播 #----------------------------# def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) x = self.ful1(x) x = self.drop(x) output = self.ful2(x) return output cnn = CNN() # 指定优化器 optimizer = torch.optim.Adam(cnn.parameters(), lr=1e-3) # 指定loss函数 loss_func = nn.CrossEntropyLoss() for epoch in range(EPOCH): for step, (b_x, b_y) in enumerate(train_loader): #----------------------------# #   计算loss并修正权值 #----------------------------# output = cnn(b_x) loss = loss_func(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() #----------------------------# #   打印 #----------------------------# if step % 50 == 0: test_output = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.numpy() accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) print('Epoch: %2d'% epoch, ', loss: %.4f' % loss.data.numpy(), ', accuracy: %.4f' % accuracy)